Rheumatoid arthritis reduces the risk of colorectal cancer through immune inflammation mediation.

There is a close relationship between immune-mediated inflammation and cancer, and there is still controversy over whether rheumatoid arthritis (RA) increases the risk of malignancy. We first used Mendelian randomization (MR) analysis to explore the potential causal relationship between RA and pan-cancer. And verify the effect of immune-mediated inflammation on cancer through intermediate MR analysis. Then we extracted the standardized incidence rate of malignancy in RA patients relative to the general population through large-scale meta-analysis. Finally, we performed pan-cancer analysis on the RA related genes obtained from MR analysis. And perform immune related analysis on key genes to reveal the association between RA and malignancy. The MR analysis demonstrated a negative correlation between RA and pan-cancer (p = 0.008). Autoimmune traits were the main mediating variable for the causal relationship between RA and pan-cancer. Based on the results of the meta-analysis, we validated that RA reduces the risk of developing colorectal cancer (SIR = 0.69, 95% CI 0.53-0.85). Pan-cancer analysis also showed that high expression of RA related genes was negatively correlated with colon adenocarcinoma. IL6R was the gene with the highest correlation among them, and its correlation with immune cells was higher in colorectal cancer than in other malignancy. Our MR study provides evidence that RA was associated with reduced risk of colorectal cancer. This effect is caused by immune-mediated inflammation, with IL6R being a key regulatory gene.

Unveiling the Structure of Oxygen Vacancies in Bulk Ceria and the Physical Mechanisms behind Their Formation.

Understanding the structures of oxygen vacancies in bulk ceria is crucial as they significantly impact the material's catalytic and electronic properties. The complex interaction between oxygen vacancies and Ce3+ ions presents challenges in characterizing ceria's defect chemistry. We introduced a machine learning-assisted cluster-expansion model to predict the energetics of defective configurations accurately within bulk ceria. This model effectively samples configurational spaces, detailing oxygen vacancy structures across different temperatures and concentrations. At lower temperatures, vacancies tend to cluster, mediated by Ce3+ ions and electrostatic repulsion, while at higher temperatures, they distribute uniformly due to configurational entropy. Our analysis also reveals a correlation between thermodynamic stability and the band gap between occupied O 2p and unoccupied Ce 4f orbitals, with wider band gaps indicating higher stability. This work enhances our understanding of defect chemistry in oxide materials and lays the groundwork for further research into how these structural properties affect ceria's performance.

Boosting Up Electrosynthesis of Urea with Nitrate and Carbon Dioxide via Synergistic Effect of Metallic Iron Cluster and Single-Atom.

Electrocatalytic conversion of nitrates and carbon dioxide to urea under ambient conditions shows promise as a potential substitute for traditional urea synthesis processes characterized by high consumption and pollution. In this study, a straightforward one-pot method is employed to prepare a highly efficient FeNC-Fe1N4 electrocatalyst, consisting of atomically dispersed Fe1N4 sites and metallic Fe clusters (FeNC) with particle size of 4-7 nm. The FeNC-Fe1N4 catalyst exhibits remarkable electrocatalytic activity for urea synthesis from nitrate anion (NO3 -) and carbon dioxide (CO2), achieving a urea production rate of 38.2 mmol gcat -1 h-1 at -0.9 V (vs RHE) and a Faradaic efficiency of 66.5% at -0.6 V (vs RHE). Both experimental and theoretical results conclusively demonstrate that metallic Fe clusters and Fe1N4 species provide active sites for the adsorption and activation of NO3 - and CO2, respectively, and the synergistic effect between Fe1N4 and metallic Fe clusters significantly enhances the electrochemical efficiency of urea synthesis. In all, this work contributes to the rational design and comprehensive synthesis of a dual-active site iron-based electrocatalyst, facilitating efficient and sustainable urea synthesis.

Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer.

OBJECTIVE: Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN: We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS: The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION: These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.

New Classification of Rheumatoid Arthritis Based on Immune Cells and Clinical Characteristics.

Background: Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but there are differences in clinical manifestations and serum test results among different patients. Methods: This is a bioinformatics study. We first obtained the gene expression profile of RA and normal synovium from the database, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR. Results: C1 subtype is related to the increase of neutrophils, C-reactive protein and erythrocyte sedimentation rate, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. CD4 T cells, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes. Conclusion: In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients.

Background: According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. Methods: In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. Results: Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. Conclusions: Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

2D Ultrathin Titanium Nitride Nanosheets as Separator Coatings for Li-S Batteries.

Transition metal nitrides (TMNs) are affirmed to be an appealing candidate for boosting the performance of lithium-sulfur (Li-S) batteries due to their excellent conductivity, strong interaction with sulfur species, and the effective catalytic ability for conversion of polysulfides. However, the traditional bulk TMNs are difficult to achieve large active surface area and fast transport channels for electrons/ions simultaneously. Here, a 2D ultrathin geometry of titanium nitride (TiN) is realized by a facile topochemical conversion strategy, which can not only serve as an interconnected conductive platform but also expose abundant catalytic active sites. The ultrathin TiN nanosheets are coated on a commercial separator, serving as a multifunctional interlayer in Li-S batteries for hindering the polysulfide shuttle effect by strong capture and fast conversion of polysulfides, achieving a high initial capacity of 1357 mAh g-1 at 0.1 C and demonstrating a low capacity decay of only 0.046% per cycle over 1000 cycles at 1 C.